Figures for manuscript

Load libraries and public variables

```r
# File that contains simulation output data in a HDF5 directory structure specified in \/publicVariables/createPublicVariables.Rmd\
simOutputH5 <- paste0(\../../dataProcessing/simOutputH5Data/\, experimentID, \_output.h5\)

# A comprehensive table that contains all simulation input and calculated output metrics
allSimInputOutput <- read_feather(\../allSimInputOutput.feather\)

# This folder contains raw output files from the simulations. This is needed for calculate_para_sp_mcpsr()
rawSimOutFolder <- \/team/batch_SMOTNT/experiment1_output\

# Where figures are stored
figureBaseFolder <- \../figures/\

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Validation: compare the protein synthesis rates with Weinberg 2016

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```r
```r
weinbergData <- read_tsv(\../externalData/weinberg_synthesis.txt\)

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Rows: 4839 Columns: 16 ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Delimiter: \t
chr (1): ORF dbl (15): Length, RPF, mRNA, TE, IP, FEatg, FEcap, cRPF, cTE, uATG, utr, gc, pE, Ai, S

ℹ Use spec() to retrieve the full column specification for this data. ℹ Specify the column types or set show_col_types = FALSE to quiet this message.

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```r
```r
validationPlot <- allSimInputOutput %>% 
  filter(paraCombo == \mRNAconstant\) %>% 
  select(ORF, paraCombo, MPSR, dc, r) %>%
  left_join(weinbergData[, c(\ORF\, \S\)], by = \ORF\) %>%
  mutate(S_perMin=S*60,
         newcombo=factor(paraCombo, levels=dc.r.df$paraCombo[2:25])) %>%
  ggplot(aes(x=MPSR, y=S_perMin)) +
    geom_abline(linetype=\dashed\) +
    geom_point() +
    geom_smooth(method=\lm\) +
    stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'
Warning: Removed 123 rows containing non-finite values (stat_smooth).
Warning: Removed 123 rows containing non-finite values (stat_cor).
Warning: Removed 123 rows containing missing values (geom_point).

```r
write_rds(validationPlot, \../figures/validationPlot.rds\)


tmp <- allSimInputOutput %>% 
    filter(paraCombo == \mRNAconstant\) %>% 
    select(ORF, paraCombo, MPSR) %>%
    left_join(weinbergData[, c(\ORF\, \S\)], by = \ORF\) %>%
    mutate(S_perMin=S*60) 

# quick try of piping the t test didn't work..
t.test(tmp$S_perMin, tmp$MPSR, paired = TRUE)

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Paired t-test

data: tmp\(S_perMin and tmp\)MPSR t = 2.7885, df = 4715, p-value = 0.005317 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.7112053 4.0789951 sample estimates: mean of the differences 2.3951

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```r
```r
figS0_a <- allSimInputOutput %>%
    filter(paraCombo==\mRNAconstant\|paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    ggplot(aes(x=MPSR, y=PMPSR, color = paraCombo)) +
    geom_point(size = 0.5) +
    geom_abline(linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'Mean of protein synthesis rate'~(min^-1),
             y = 'Predicted mean of protein synthesis rate'~(min^-1)) +
        scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth(method=\lm\) +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figS0_a,\../figures/figS0_a.rds\)
######################################################
figS0_b <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, MVPS, PMPSR, mRNASynRateTotal_sec) %>%
    mutate(mRNASynRateTotal_min = mRNASynRateTotal_sec*60,
           additiveVar = PMPSR + mRNASynRateTotal_min,
           multiplicativeVar = PMPSR * mRNASynRateTotal_min + PMPSR^2*mRNASynRateTotal_min + PMPSR*mRNASynRateTotal_min^2) %>% 
    select(ORF, MVPS, additiveVar, multiplicativeVar) %>% 
    pivot_longer(names_to = \predictedVar\, values_to = \predictedVarValue\, cols = -c(ORF,MVPS)) %>% 
    ggplot(aes(x=MVPS, y=predictedVarValue, color = predictedVar)) +
    geom_point(size = 0.5) +
    geom_abline(linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'Variance of protein synthesis rate'~(min^-1),
             y = 'Predicted variance of protein synthesis rate'~(min^-1)) +
        scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth(method=\lm\) +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
`geom_smooth()` using formula 'y ~ x'
Warning: Removed 31 rows containing non-finite values (stat_smooth).
Warning: Removed 31 rows containing non-finite values (stat_cor).

```r
write_rds(figS0_b, \../figures/figS0_b.rds\)
######################################################
figS0_c <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, MPSR, RMPSR) %>% 
    ggplot(aes(x = MPSR, y = RMPSR)) +
    geom_point(size = 0.5) +
    #geom_abline(linetype=\dashed\) +
    geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'mean prot syn rate for mRNAvarying'~(min^-1),
             y = 'relative mean prot syn rate for mRNAvarying'~(min^-1)) +
        #scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth() +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = \cs\)'

```r
write_rds(figS0_c, \../figures/figS0_c.rds\)
######################################################
figS0_d <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, MPSR, RMPSR) %>%
    arrange(MPSR) %>% 
    # mutate(binNames = c(unlist(lapply(1:19, function(x){rep(x,254)})), rep(20,12))) %>%  # bin all 4838 genes into 20 bins, each contains same number of genes ordered by their MPSR
    # ggplot(aes(x = factor(binNames, levels = 1:20), y = RMPSR)) +
    mutate(binNames = c(unlist(lapply(1:9, function(x){rep(x,537)})), rep(10,5))) %>%  # bin all 4838 genes into 10 bins, each contains same number of genes ordered by their MPSR
    ggplot(aes(x = factor(binNames, levels = 1:10), y = RMPSR)) +
    geom_boxplot() +
    #geom_abline(linetype=\dashed\) +
    geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'mean prot syn rate for mRNAvarying'~(min^-1),
             y = 'relative mean prot syn rate for mRNAvarying'~(min^-1)) +
        #scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth() +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

```r
write_rds(figS0_d, \../figures/figS0_d.rds\)
######################################################
figS0_e <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, MPSR, RMPSR) %>%
    arrange(MPSR) %>% 
    # mutate(binNames = c(unlist(lapply(1:19, function(x){rep(x,254)})), rep(20,12))) %>%  # bin all 4838 genes into 20 bins, each contains same number of genes ordered by their MPSR
    # ggplot(aes(x = factor(binNames, levels = 1:20), y = RMPSR)) +
    mutate(binNames = c(unlist(lapply(1:9, function(x){rep(x,537)})), rep(10,5))) %>%  # bin all 4838 genes into 10 bins, each contains same number of genes ordered by their MPSR
    group_by(binNames) %>% 
    mutate(test = RMPSR>1) %>% 
    #mutate(freqRMPSRabove1 = sum(test)/n()) %>% 
    summarize(freqRMPSRabove1 = sum(test)/n()) %>% 
    ggplot(aes(x = factor(binNames, levels = 1:10), y = freqRMPSRabove1)) +
    geom_bar(stat='identity') +
    #geom_abline(linetype=\dashed\) +
    #geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'bins of genes',
             y = 'freq of rel. mean prot syn rate above 1 in each bin')

figS0_e

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<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
write_rds(figS0_e, \../figures/figS0_e.rds\)
######################################################
figS0_f <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, mRNAabundance, RMPSR) %>% 
    ggplot(aes(x = mRNAabundance, y = RMPSR)) +
    geom_point(size = 0.5) +
    #geom_abline(linetype=\dashed\) +
    geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'mRNA abundances',
             y = 'relative mean prot syn rate for mRNAvarying'~(min^-1)) +
        #scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth() +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = \cs\)'

```r
write_rds(figS0_f, \../figures/figS0_f.rds\)
######################################################
figS0_g <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, CAI, RMPSR) %>% 
    ggplot(aes(x = CAI, y = RMPSR)) +
    geom_point(size = 0.5) +
    #geom_abline(linetype=\dashed\) +
    geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'CAI',
             y = 'relative mean prot syn rate for mRNAvarying'~(min^-1)) +
        #scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth() +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = \cs\)'

```r
write_rds(figS0_g, \../figures/figS0_g.rds\)
######################################################
figS0_h <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, iniProb, RMPSR) %>% 
    ggplot(aes(x = iniProb, y = RMPSR)) +
    geom_point(size = 0.5) +
    #geom_abline(linetype=\dashed\) +
    geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'translation initiation probability',
             y = 'relative mean prot syn rate for mRNAvarying'~(min^-1)) +
        #scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth() +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = \cs\)'

```r
write_rds(figS0_h, \../figures/figS0_h.rds\)
######################################################
figS0_i <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, geneLength_codon, RMPSR) %>% 
    ggplot(aes(x = geneLength_codon, y = RMPSR)) +
    geom_point(size = 0.5) +
    #geom_abline(linetype=\dashed\) +
    geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'gene length (codon)',
             y = 'relative mean prot syn rate for mRNAvarying'~(min^-1)) +
        #scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth() +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = \cs\)'

```r
write_rds(figS0_i, \../figures/figS0_i.rds\)
######################################################
figS0_j <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\) %>%
    select(paraCombo, ORF, mRNADecRateNeymotin_min, RMPSR) %>% 
    ggplot(aes(x = mRNADecRateNeymotin_min, y = RMPSR)) +
    geom_point(size = 0.5) +
    #geom_abline(linetype=\dashed\) +
    geom_hline(yintercept = 1, linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \top\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x = 'mRNA decay rates from Neymotin data (min)',
             y = 'relative mean prot syn rate for mRNAvarying'~(min^-1)) +
        #scale_color_manual(name=\\, labels=c(\mRNAconstant\=\mRNAconstant\, \dc_1_r_0\=\mRNAvarying\), values = c(\#1F78B4\, \#FF7F00\)) +
        geom_smooth() +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = \cs\)'
Warning: Removed 31 rows containing non-finite values (stat_smooth).
Warning: Removed 31 rows containing non-finite values (stat_cor).

```r
write_rds(figS0_j, \../figures/figS0_j.rds\)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




Fig S1

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
```r
trial_mRNAcount <- read_feather(\../../simulations/trialmRNAcountData/trial_mRNAcount.feather\)
trial_mRNAcount

figureS1 <- allSimInputOutput %>%
    left_join(., trial_mRNAcount[,c(\paraCombo\,\ORF\,\trialRMMC\)], by = c(\paraCombo\=\paraCombo\, \ORF\=\ORF\)) %>%
    filter(paraCombo != \mRNAconstant\) %>%
    select(paraCombo, RMMC, trialRMMC, dc, r) %>%
    `colnames<-`(c(\paraCombo\, \AfterScaling\, \BeforeScaling\, \dc\, \r\)) %>%
    pivot_longer(names_to = \key\, values_to = \RMMCvalue\, cols = 2:3) %>%
    ggplot(aes(x=RMMCvalue, color=key)) +
        geom_density() +
        facet_grid(dc~r, labeller=labeller(dc=c(\1\=\100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\Co-trns 0%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) + 
        labs(title = \Fig S1: mRNA counts when varying are laregely equal to when they are constant \nafter scaling the mRNA synthesis rates\, 
             x=\Relative mean mRNA count\) +
        scale_y_continuous() +
        theme_bw() +
        theme(text=element_text(size=13), 
              legend.title = element_blank(), 
              legend.position = \bottom\)

figureS1

<!-- rnb-source-end -->

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_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	   
dc_0.2_r_1  	   
dc_0.4_r_0  	   dc_0.4_r_0.1  	   dc_0.4_r_0.4  	   
dc_0.4_r_1  	   
dc_0.6_r_0  	   dc_0.6_r_0.1  	   dc_0.6_r_0.4  	   
dc_0.6_r_1  	   
dc_0.8_r_0  	   dc_0.8_r_0.1  	   dc_0.8_r_0.4  	   
dc_0.8_r_1  	   dc_1_r_0  	   
dc_1_r_0.1  	   
dc_1_r_0.4  	   dc_1_r_1  	   mRNAconstant  	   dc_0_r_0  	   
dc_0_r_0.1  	   
dc_0_r_0.4  	   dc_0_r_1  	   
dc_0.2_r_0  	   dc_0.2_r_0.1  	   dc_0.2_r_0.4  	  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/>

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r

write_rds(figureS1, \../figures/figureS1.rds\)
#ggsave(paste0(figureBaseFolder, \figures1/s_fig_1.png\), dpi=100, height = 6, width = 8)

###########################################################
allSimInputOutput %>% 
  filter(paraCombo != \mRNAconstant\) %>% 
  select(paraCombo, RMMC, dc, r) %>% 
  ggplot(aes(x = RMMC)) + 
      geom_density() +
      facet_grid(dc~r, labeller=labeller(dc=c(\1\=\100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\Co-trns 0%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) + 
      labs(x=\Relative mean mRNA count after scaling\) +
      scale_y_continuous() +
      theme_bw() +
      theme(text=element_text(size=13), 
            legend.title = element_blank(), 
            legend.position = \none\) +
      geom_vline(xintercept = 1, linetype = \dashed\)

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r


###########################################################
allSimInputOutput %>%
    filter(paraCombo != \mRNAconstant\) %>%
    select(paraCombo, RMMC, dc, r, geneLength_codon) %>%
    mutate(sORl = ifelse(geneLength_codon >=512, \long\, \short\)) %>%
    ggplot(aes(x=RMMC, color=sORl)) +
        geom_density() +
        facet_grid(dc~r, labeller=labeller(dc=c(\1\=\100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\Co-trns 0%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) + 
        labs(x=\Relative mean mRNA count\) +
        scale_y_continuous() +
        theme_bw() +
        theme(legend.title = element_blank(), 
              legend.position = \bottom\, 
              strip.text.x = element_text(size=10), 
              strip.text.y = element_text(size=10))

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r
###########################################################
allSimInputOutput %>%
    filter(paraCombo != \mRNAconstant\) %>%
    select(paraCombo, RMMC, dc, r, mRNADecRateNeymotin_sec) %>%
    mutate(fORs = ifelse(mRNADecRateNeymotin_sec >=0.0009431628, \fast\, \slow\)) %>%
    ggplot(aes(x=RMMC, color=fORs)) +
        geom_density() +
        facet_grid(dc~r, labeller=labeller(dc=c(\1\=\100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\Co-trns 0%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) + 
        labs(x=\Relative mean mRNA count\) +
        scale_y_continuous() +
        theme_bw() +
        theme(legend.title = element_blank(), 
              legend.position = \bottom\, 
              strip.text.x = element_text(size=10), 
              strip.text.y = element_text(size=10))

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin 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 -->

```r
```r


allSimInputOutput%>%
  filter(ORF!=\YER053C-A\, mRNADecRateNeymotin_sec!=0, paraCombo!=\mRNAconstant\) %>%
  mutate(dc_fct=factor(dc, levels=c(\1\, \0.8\, \0.6\, \0.4\, \0.2\, \0\))) %>%
  ggplot(aes(x=dc_fct, y=RMMC, fill=r)) +
  geom_boxplot(outlier.size = 0.1) +
  # geom_hline(aes(yintercept=median(as.numeric(unlist(allSimInputOutput%>%filter(ORF!=\YER053C-A\, mRNADecRateNeymotin_sec!=0, paraCombo==\dc_1_r_0\)%>%select(RMMDR))), na.rm = TRUE)), color=\red\, linetype=\dashed\) +
  theme_bw() +
  labs(x=\CMD level\,
       y=\Relative mean mRNA count\) +
  scale_x_discrete(labels=c(\1\=\100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\0\)) +
  scale_fill_discrete(name = \Ribosome\nprot index\) +
  theme(
    text = element_text(size = 13),
    legend.position = \none\
  )

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuICBcblxuYWxsU2ltSW5wdXRPdXRwdXQgJT4lXG4gIGdyb3VwX2J5KHBhcmFDb21ibykgJT4lXG4gIHN1bW1hcmlzZShNID0gbWVhbihSTU1DKSkgJT4lXG4gIGFycmFuZ2UoZGVzYyhNKSlcbmBgYFxuYGBgclxuXG5hbGxTaW1JbnB1dE91dHB1dCAlPiVcbiAgZ3JvdXBfYnkoZGMpICU+JVxuICBzdW1tYXJpc2UoTSA9IG1lYW4oUk1NQykpICU+JVxuICBhcnJhbmdlKGRlc2MoTSkpXG5gYGBcbmBgYCJ9 -->

```r
```r
  

allSimInputOutput %>%
  group_by(paraCombo) %>%
  summarise(M = mean(RMMC)) %>%
  arrange(desc(M))

allSimInputOutput %>%
  group_by(dc) %>%
  summarise(M = mean(RMMC)) %>%
  arrange(desc(M))

<!-- rnb-source-end -->

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```r
```r
NA

<!-- rnb-source-end -->

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<!-- rnb-frame-end -->

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<!-- rnb-text-begin -->




Fig. was S3, now S2

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
```r
figureS2 <- allSimInputOutput %>%
  filter(paraCombo != \mRNAconstant\, ORF != \YER053C-A\, mRNADecRateNeymotin_sec != 0) %>%
  ggplot(aes(x = MMC, y = MVMC)) +
  geom_point(size = 0.5) +
  geom_abline(linetype = \dashed\) +
  facet_grid(dc~r, labeller = labeller(dc = c(\1\ = \100%\, \0.8\ = \80%\, \0.6\ = \60%\, \0.4\ = \40%\, \0.2\ = \20%\, \0\ = \Co-trns 0%\), r = c(\0\ = \Ribo prot indx  =  0\, \0.1\ = \0.1\, \0.4\ = \0.4\, \1\ = \1\), label_parsed)) +
  theme_bw() +
  theme(text = element_text(size = 13),
            legend.position = \none\,
            plot.margin = margin(r = 0, unit = \pt\),
            panel.grid = element_blank()
    ) +
      labs(x = 'Mean of mRNA count',
           y = 'Variance of mRNA count') +
      geom_smooth(method = \lm\) +
      stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS2, \../figures/figureS2.rds\)

<!-- rnb-source-end -->

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<!-- rnb-text-begin -->





was Fig S4, now S3: Sequence features correlate with the fano factor of protein synthesis rates for mRNA constant

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
```r
plot_fun1 <- function(var1, var2){ allSimInputOutput%>%  #var1:\MeanProteinSynRate\.. var2:\mRNAconstant\, \dc_1_r_0\
  filter(paraCombo == var2, mRNADecRateNeymotin_sec != 0) %>%
  select(ORF, MPSR, MVPS, iniProb_log10, mRNAabundance_log10, mRNADecRateNeymotin_sec_log10, CAI, geneLength_codon_log10) %>%
  mutate(fano_protSyn = MVPS/MPSR) %>%
  filter(fano_protSyn>0.3) %>%  # filter out the two outliers
  `colnames<-`(paste0(\\,c(\ORF\, \MeanProteinSynRate\, \VarianceProteinSynRate\, \iniProb_log10\, \mRNALevel_log10\, \DecRate_log10\, \CAI\, \geneLength_log10\, \fano_protSynRate\))) %>%
  pivot_longer(names_to = \feature\, values_to = \featureVals\, !c(ORF,MeanProteinSynRate, VarianceProteinSynRate, fano_protSynRate)) %>%
  ggplot(aes_string(x = \featureVals\, y = var1)) +
    geom_point(size = 0.5) +
    facet_wrap(~feature, scales = \free\, nrow = 1, strip.position = \bottom\) +
    theme_bw() +
    geom_smooth(method = \lm\) +
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS3, \../figures/figureS3.rds\)

<!-- rnb-source-end -->

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<!-- rnb-text-begin -->


Was Fig S5, now S4. 

<!-- rnb-text-end -->


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```r
```r
# MMDP = mRNAs marked for degradation proportion among mean total mRNA
figureS4 <- allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0) %>%
    ggplot(aes(x = MMDP, y = RMPSR)) +
    geom_point(size = 0.1) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \Proportion of mRNA marked for decay\, 
        y = \Relative mean of protein synthesis rates\, 
        title = \\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS4, \../figures/figureS4.rds\)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Fig. S5

<!-- rnb-text-end -->


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```r
```r
figureS5 <- allSimInputOutput %>%
    filter(paraCombo==\dc_1_r_0\, ORF!=\YER053C-A\, mRNADecRateNeymotin_sec!=0) %>% 
    select(\ORF\, \CVPS\, \CVMC\, \paraCombo\) %>% #remove the gene with the 450min HL
    ggplot(aes(x=CVMC, y=CVPS, colour=paraCombo)) +
    geom_point(size = 0.5) +
    geom_abline(linetype=\dashed\) +
    theme_bw() +
    theme(text = element_text(size = 13),
              legend.position = \none\,
              strip.text = element_text(color=\white\),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank()
      ) +
        labs(x =\CV for mRNA count per min\,
             y = \CV for protein synthesis rate\) +
        #scale_color_discrete(name=\\,labels=c(\Co-trans 100% RiboProtect 0\)) +
        geom_smooth(method=\lm\) +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS5, \../figures/figureS5.rds\)

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<!-- rnb-text-begin -->



Fig S6: relative CV = CV of the protein synthesis rate/CV of the mRNA count

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<!-- rnb-source-begin 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 -->

```r
```r
figureS6 <- allSimInputOutput %>%
  filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YCR024C-B\) %>%    #\YCR024C-B\ is an outlier whose relCV = 23
  mutate(relCV = CVPS/CVMC) %>%
  select(ORF, relCV, iniProb_log10, mRNAabundance_log10, mRNADecRateNeymotin_sec_log10, CAI, geneLength_codon_log10) %>%
    `colnames<-`(paste0(\\, c(\ORF\, \Relative_CV\, \iniProb_log10\, \mRNALevel_log10\, \DecRate_log10\, \CAI\, \geneLength_log10\))) %>%
  pivot_longer(names_to = \feature\, values_to = \featureVals\, -c(ORF, Relative_CV)) %>%
  ggplot(aes(x = featureVals, y = Relative_CV)) +
    geom_point(size = 0.5) +
      facet_wrap(~feature, scales = \free\, nrow = 1, strip.position = \bottom\) +
      theme_bw() +
      geom_smooth(method = \lm\) +
      stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS6, \../figures/figureS6.rds\)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


Fig. S7: 

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin 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 -->

```r
```r
figureS7 <- allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !geneLength_codon) %>%
    ggplot(aes(x = featureVals, y = geneLength_codon)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Gene length (codon)\,
        title = \dc_1_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS7, \../figures/figureS7.rds\)
###################################################

allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_1\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\,ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !geneLength_codon) %>%
    ggplot(aes(x = featureVals, y = geneLength_codon)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Gene length (codon)\,
        title = \dc_1_r_1\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
tmp <- allSimInputOutput %>%
       filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\,ORF != \YGR169C-A\, ORF != \YLR106C\,ORF != \YCR024C-B\) %>%
       mutate(RDR = MMDR/mRNADecRateNeymotin_sec) %>%
       select(RMTE, geneLength_codon, RDR) 
summary(lm(RMTE~geneLength_codon+RDR, tmp))

<!-- rnb-source-end -->

<!-- rnb-output-begin 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 -->

Call: lm(formula = RMTE ~ geneLength_codon + RDR, data = tmp)

Residuals: Min 1Q Median 3Q Max -0.080870 -0.005794 0.000774 0.008152 0.075226

Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.320e-01 9.835e-03 43.92 <2e-16 geneLength_codon -2.947e-05 1.010e-06 -29.18 <2e-16 RDR 6.010e-01 9.869e-03 60.90 <2e-16 *** — Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.01439 on 4801 degrees of freedom Multiple R-squared: 0.8319, Adjusted R-squared: 0.8319 F-statistic: 1.188e+04 on 2 and 4801 DF, p-value: < 2.2e-16

<!-- rnb-output-end -->

<!-- rnb-source-begin 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 -->

```r
```r
# MMDP = mRNAs marked for degradation proportion among mean total mRNA
# MTT = mean translation time
allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0) %>%
    ggplot(aes(x = MTT, y = MMDP)) +
    geom_point(size = 0.1) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \Mean translation time\,
        y = \Proportion of mRNA marked for decay\,
        title = \\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0.2_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !geneLength_codon) %>%
    ggplot(aes(x = featureVals, y = geneLength_codon)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Gene length (codon)\,
        title = \dc_0.2_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0.2_r_1\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !geneLength_codon) %>%
    ggplot(aes(x = featureVals, y = geneLength_codon)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Gene length (codon)\,
        title = \dc_0.2_r_1\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
`geom_smooth()` using formula 'y ~ x'
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing non-finite values (stat_cor).

Fig. S8

```r
figureS8 <- allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_1_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS8, \../figures/figureS8.rds\)

#############################################

allSimInputOutput %>%
    filter(paraCombo == \dc_0.8_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_0.8_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0.6_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_0.6_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0.4_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1,strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_0.4_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0.2_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_0.2_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_0_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
`geom_smooth()` using formula 'y ~ x'
Warning: Removed 4804 rows containing non-finite values (stat_smooth).
Warning: Removed 4804 rows containing non-finite values (stat_cor).

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_1\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_1_r_1\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0.2_r_1\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
    `colnames<-` (c(\RelMeanTE\, \MeanTransTime\, \mRNAMarkdDecProp\, \geneLength_codon\, \RelativeDecRate\)) %>%
    pivot_longer(names_to = \feature\, values_to = \featureVals\, !RelMeanTE) %>%
    ggplot(aes(x = featureVals, y = RelMeanTE)) +
    geom_point(size = 0.1) +
    facet_wrap(~feature, scales = \free_x\, nrow = 1, strip.position = \bottom\) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \\,
        y = \Relative mean TE\,
        title = \dc_0.2_r_1\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
`geom_smooth()` using formula 'y ~ x'
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing non-finite values (stat_cor).

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(MMDP, RMMDR) %>%
    ggplot(aes(x = MMDP, y = RMMDR)) +
    geom_point(size = 0.1) +
    labs(x = \MMDP\,
        y = \Relative decay rates\,
        title = \dc_1_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
# MMDP = mRNAs marked for degradation proportion
allSimInputOutput %>%
    filter(paraCombo == \dc_0.2_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
    select(MMDP, RMMDR) %>%
    ggplot(aes(x = MMDP, y = RMMDR)) +
    geom_point(size = 0.1) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x = \MMDP\,
        y = \Relative decay rates\,
        title = \dc_0.2_r_0\) +
    geom_smooth(method = \lm\) + 
    stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

Fig. S9

```r
figureS9 <- allSimInputOutput %>%
  filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\, dc != 0) %>%
  #select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
  ggplot(aes(x = MMDP, y = RMTE)) +
  geom_point(size = 0.1) +
  facet_grid(dc~r, labeller = labeller(dc = c(\1\ = \100%\, \0.8\ = \80%\, \0.6\ = \60%\, \0.4\ = \40%\, \0.2\ = \Co-trns 20%\), r = c(\0\ = \Ribo prot indx  =  0\, \0.1\ = \0.1\, \0.4\ = \0.4\, \1\ = \1\), label_parsed)) +
  theme_bw() +
  theme(text = element_text(size = 13),
            legend.position = \none\,
            plot.margin = margin(r = 0, unit = \pt\),
            panel.grid = element_blank()
    ) +
      labs(x = 'Proportions of mRNAs undergoing CMD for all genes',
           y = 'Relative mean translation efficiencies') +
      geom_smooth(method = \lm\) +
      stat_cor(aes(label = paste(..r.label..,  sep = \~`

Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
`geom_smooth()` using formula 'y ~ x'
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing non-finite values (stat_cor).

```r
write_rds(figureS9, \../figures/figureS9.rds\)

##############################################################

allSimInputOutput %>%
  filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\, , r==0|r==1, dc==0.2|dc==1) %>%
  #select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
  ggplot(aes(x = MMDP, y = RMTE)) +
  geom_point(size = 0.1) +
  facet_grid(dc~r, labeller = labeller(dc = c(\1\ = \100%\, \0.8\ = \80%\, \0.6\ = \60%\, \0.4\ = \40%\, \0.2\ = \Co-trns 20%\), r = c(\0\ = \Ribo prot indx  =  0\, \0.1\ = \0.1\, \0.4\ = \0.4\, \1\ = \1\), label_parsed)) +
  theme_bw() +
  theme(text = element_text(size = 13),
            legend.position = \none\,
            plot.margin = margin(r = 0, unit = \pt\),
            panel.grid = element_blank()
    ) +
      labs(x = 'Proportions of mRNAs undergoing CMD for all genes',
           y = 'Relative mean translation efficiencies') +
      geom_smooth(method = \lm\) +
      stat_cor(aes(label = paste(..r.label..,  sep = \~`

Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
`geom_smooth()` using formula 'y ~ x'
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing non-finite values (stat_cor).

Fig. S10

```r
figureS10 <- allSimInputOutput %>%
  filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\) %>%
  #select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
  ggplot(aes(x = RMMDR, y = RMTE)) +
  geom_point(size = 0.1) +
  facet_grid(dc~r, labeller = labeller(dc = c(\1\ = \100%\, \0.8\ = \80%\, \0.6\ = \60%\, \0.4\ = \40%\, \0.2\ = \20%\, \0\ = \Co-trns 0%\), r = c(\0\ = \Ribo prot indx  =  0\, \0.1\ = \0.1\, \0.4\ = \0.4\, \1\ = \1\), label_parsed)) +
  theme_bw() +
  theme(text = element_text(size = 13),
            legend.position = \none\,
            plot.margin = margin(r = 0, unit = \pt\),
            panel.grid = element_blank()
    ) +
      labs(x = 'Relative mRNA decay rates',
           y = 'Relative mean translation efficiencies') +
      geom_smooth(method = \lm\) +
      stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
write_rds(figureS10, \../figures/figureS10.rds\)
#########################################################

allSimInputOutput %>%
  filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, ORF != \YGR169C-A\, ORF != \YLR106C\, ORF != \YCR024C-B\, r==0|r==1, dc==0|dc==1) %>%
  #select(RMTE, MTT, MMDP, geneLength_codon, RMMDR) %>%
  ggplot(aes(x = RMMDR, y = RMTE)) +
  geom_point(size = 0.1) +
  facet_grid(dc~r, labeller = labeller(dc = c(\1\ = \100%\, \0.8\ = \80%\, \0.6\ = \60%\, \0.4\ = \40%\, \0.2\ = \20%\, \0\ = \Co-trns 0%\), r = c(\0\ = \Ribo prot indx  =  0\, \0.1\ = \0.1\, \0.4\ = \0.4\, \1\ = \1\), label_parsed)) +
  theme_bw() +
  theme(text = element_text(size = 13),
            legend.position = \none\,
            plot.margin = margin(r = 0, unit = \pt\),
            panel.grid = element_blank()
    ) +
      labs(x = 'Relative mRNA decay rates',
           y = 'Relative mean translation efficiencies') +
      geom_smooth(method = \lm\) +
      stat_cor(aes(label = paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

was Fig. S9, maybe should not use it coz the vline calculated from its input decay rate may not be accurate.

```r
lifetimeDistri<- function(genevec){
    dfplot <- cbind(dc.r.df[c(2:5, 22:25), ], data.frame(matrix(NA, ncol = 5000, nrow = 8*3))) %>%
        mutate(ORF = c(rep(genevec[1], 8), rep(genevec[2], 8), rep(genevec[3], 8))) %>%
        relocate(ORF)
    
    for(i in 1:8){dfplot[i, 6:5005] = as.vector(h5read(simOutputH5, paste0(genevec[1], \/mRNA_lifeTimes/\, dfplot$paraCombo[i])))}
    for(i in 9:16){dfplot[i, 6:5005] = as.vector(h5read(simOutputH5, paste0(genevec[2], \/mRNA_lifeTimes/\, dfplot$paraCombo[i])))}
    for(i in 17:24){dfplot[i, 6:5005] = as.vector(h5read(simOutputH5, paste0(genevec[3], \/mRNA_lifeTimes/\, dfplot$paraCombo[i])))}
    
    dfplot %>%
        pivot_longer(names_to = \varyingParaCombo\, values_to = \lifeTimes\, cols = 6:5005) %>%
        ggplot(aes(lifeTimes, fill = dc)) +
        geom_density(alpha = 0.5, color = NA) +
        facet_grid(factor(ORF, levels = genevec)~r, 
                   labeller = labeller(r = c(\0\ = \Ribo prot index 0\, \0.1\ = \0.1\, \0.4\ = \0.4\, \1\ = \1\))) +
        scale_x_log10(labels = scales::comma_format(accuracy = 1L)) +
        scale_fill_discrete(name = \CMD level\,
                            labels = c(\0\ = \0\, \0.2\ = \20%\, \0.6\ = \60%\, \1\ = \100%\)) +
        theme_bw() +
        labs(x = paste0(\mRNA life time distribution (log10)\)) +
        theme(
          legend.position = \top\,
          legend.justification = \left\,
          text = element_text(size = 13),
          axis.text.x = element_text(size = 8)) +
        geom_vline(data = filter(dfplot, ORF == genevec[1]), aes(xintercept = 1/simInputFeatures$mRNADecRateNeymotin_sec[which(simInputFeatures$ORF == genevec[1])]), linetype = \dashed\, color = \dark grey\) + #1049.856 =1/simInputFeatures$mRNADecRateNeymotin_sec[1] # 1049.856 is the input lifetime for YAL001C 
        geom_vline(data = filter(dfplot, ORF == genevec[2]), aes(xintercept = 1/simInputFeatures$mRNADecRateNeymotin_sec[which(simInputFeatures$ORF == genevec[2])]), linetype = \dashed\, color = \dark grey\) +
        geom_vline(data = filter(dfplot, ORF == genevec[3]), aes(xintercept = 1/simInputFeatures$mRNADecRateNeymotin_sec[which(simInputFeatures$ORF == genevec[3])]), linetype = \dashed\, color = \dark grey\) 
        # the above lines are the average life time calculated from input decay rates
}

lifetimeDistri(c(\YAL001C\, \YKR075C\, \YBR011C\))

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

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/>

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGlmZXRpbWVEaXN0cmkoYyhcXFlMUjEwNkNcXCwgXFxZS1IwNTRDXFwsIFxcWUhSMDk5V1xcKSkgIyBsb25nZXN0IGdlbmVzXG5gYGBcbmBgYCJ9 -->

```r
```r
lifetimeDistri(c(\YLR106C\, \YKR054C\, \YHR099W\)) # longest genes

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiV2FybmluZzogUmVtb3ZlZCA2MTg4IHJvd3MgY29udGFpbmluZyBub24tZmluaXRlIHZhbHVlcyAoc3RhdF9kZW5zaXR5KS5cbiJ9 -->

Warning: Removed 6188 rows containing non-finite values (stat_density).

<!-- rnb-output-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxubGlmZXRpbWVEaXN0cmkoYyhcXFlBUjAwMkMtQVxcLCBcXFlCTDA1MFdcXCwgXFxZQlIwMDNXXFwpKVxuXG5gYGBcbmBgYCJ9 -->

```r
```r
lifetimeDistri(c(\YAR002C-A\, \YBL050W\, \YBR003W\))

<!-- rnb-source-end -->

<!-- rnb-plot-begin -->

<img 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" />

<!-- rnb-plot-end -->

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<!-- rnb-text-begin -->



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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuYWxsU2ltSW5wdXRPdXRwdXQgJT4lXG4gICAgZmlsdGVyKHBhcmFDb21ibyA9PSBcXGRjXzBfcl8wXFwsIG1STkFEZWNSYXRlTmV5bW90aW5fc2VjICE9IDAsIG1STkFhYnVuZGFuY2U+MSwgT1JGIT1cXFlFUjA1M0MtQVxcLCBPUkYhPVxcWUxSMTA2Q1xcKSAlPiUgICNyZW1vdmUgdGhlIGdlbmUgd2l0aCBITD00NTBtaW4sIGFsc28gYW4gb3V0bGllciBnZW5lIFxcWUxSMTA2Q1xcIHdpdGggUk1URT09MC42XG4gICAgc2VsZWN0KE9SRiwgcGFyYUNvbWJvLCBSTVRFLCBtUk5BYWJ1bmRhbmNlX2xvZzEwLCBpbmlQcm9iX2xvZzEwLCBtUk5BRGVjUmF0ZU5leW1vdGluX3NlY19sb2cxMCwgQ0FJLCBnZW5lTGVuZ3RoX2NvZG9uX2xvZzEwKSAlPiVcbiAgICBgY29sbmFtZXM8LWAoYyhcXE9SRlxcLCBcXHBhcmFDb21ib1xcLCBcXFJNVEVcXCwgXFxsb2cobVJOQWFidW5kYW5jZSlcXCwgXFxsb2coSW5pUHJvYilcXCwgXFxsb2coRGVjUmF0ZSlcXCwgXFxDQUlcXCwgXFxsb2coTGVuZ3RoKVxcKSkgJT4lXG4gICAgcGl2b3RfbG9uZ2VyKGNvbHMgPSAtYyhSTVRFLCBwYXJhQ29tYm8sIE9SRiksIG5hbWVzX3RvID0gXFxmZWF0dXJlXFwsIHZhbHVlc190byA9IFxcZmVhdHVyZVZhbFxcKSAlPiVcbiAgICBnZ3Bsb3QoYWVzKHg9ZmVhdHVyZVZhbCwgeT1STVRFLCBjb2xvcj1wYXJhQ29tYm8pKSArXG4gICAgZ2VvbV9wb2ludChzaXplPTAuNSkgK1xuICAgIGZhY2V0X3dyYXAofmZlYXR1cmUsIHNjYWxlcyA9IFxcZnJlZVxcLCBucm93PTEsIHN0cmlwLnBvc2l0aW9uID0gXFxib3R0b21cXCkgK1xuICAgIHRoZW1lX2J3KCkgK1xuICAgIHNjYWxlX3lfbG9nMTAoKSArXG4gICAgdGhlbWUodGV4dCA9IGVsZW1lbnRfdGV4dChzaXplID0gMTMpLFxuICAgICAgICAgICAgICBsZWdlbmQucG9zaXRpb24gPSBcXG5vbmVcXCxcbiAgICAgICAgICAgICAgc3RyaXAudGV4dCA9IGVsZW1lbnRfdGV4dChjb2xvcj1cXGJsYWNrXFwsIHNpemU9cmVsKDEuMikpLCAgIyBjYW4ndCB1c2UgZWxlbWVudF9ibGFuaygpIGhlcmUgYmVjYXVzZSBpdCBnaXZlcyBlcnJvciB3aGVuIHAxK3AyXG4gICAgICAgICAgICAgIHBsb3QubWFyZ2luID0gbWFyZ2luKHIgPSAwLCB1bml0ID0gXFxwdFxcKSxcbiAgICAgICAgICAgICAgcGFuZWwuZ3JpZCA9IGVsZW1lbnRfYmxhbmsoKSxcbiAgICAgICAgICAgICAgc3RyaXAuYmFja2dyb3VuZCA9IGVsZW1lbnRfYmxhbmsoKSwgIFxuICAgICAgICAgICAgICBzdHJpcC5wbGFjZW1lbnQgPSBcXG91dHNpZGVcXCAgICAgI3RvIHB1dCB0aGUgc3RyaXAgbGFiZWxzIGJlbG93IHgtYXhpc1xuICAgICAgKSArXG4gICAgICAgIGxhYnMoeCA9IFxcXFwsXG4gICAgICAgICAgICAgeSA9IFxcUmVsYXRpdmUgbWVhbiB0cmFuc2xhdGlvbiBlZmZpY2llbmN5XFwsXG4gICAgICAgICAgICAgdGl0bGUgPSBcXGRjXzBfcl8wXFwpICtcbiAgICAgICAgZ2VvbV9zbW9vdGgobWV0aG9kPVxcbG1cXCkgK1xuICAgICAgICBzdGF0X2NvcihhZXMobGFiZWw9cGFzdGUoLi5yLmxhYmVsLi4sICBzZXAgPSBcXH5gXG5gYGAifQ== -->

```r
```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0_r_0\, mRNADecRateNeymotin_sec != 0, mRNAabundance>1, ORF!=\YER053C-A\, ORF!=\YLR106C\) %>%  #remove the gene with HL=450min, also an outlier gene \YLR106C\ with RMTE==0.6
    select(ORF, paraCombo, RMTE, mRNAabundance_log10, iniProb_log10, mRNADecRateNeymotin_sec_log10, CAI, geneLength_codon_log10) %>%
    `colnames<-`(c(\ORF\, \paraCombo\, \RMTE\, \log(mRNAabundance)\, \log(IniProb)\, \log(DecRate)\, \CAI\, \log(Length)\)) %>%
    pivot_longer(cols = -c(RMTE, paraCombo, ORF), names_to = \feature\, values_to = \featureVal\) %>%
    ggplot(aes(x=featureVal, y=RMTE, color=paraCombo)) +
    geom_point(size=0.5) +
    facet_wrap(~feature, scales = \free\, nrow=1, strip.position = \bottom\) +
    theme_bw() +
    scale_y_log10() +
    theme(text = element_text(size = 13),
              legend.position = \none\,
              strip.text = element_text(color=\black\, size=rel(1.2)),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank(),
              strip.background = element_blank(),  
              strip.placement = \outside\     #to put the strip labels below x-axis
      ) +
        labs(x = \\,
             y = \Relative mean translation efficiency\,
             title = \dc_0_r_0\) +
        geom_smooth(method=\lm\) +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_0_r_1\, mRNADecRateNeymotin_sec != 0, mRNAabundance>1, ORF!=\YER053C-A\, ORF!=\YLR106C\) %>%  #remove the gene with HL=450min, also an outlier gene \YLR106C\ with RMTE==0.6
    select(ORF, paraCombo, RMTE, mRNAabundance_log10, iniProb_log10, mRNADecRateNeymotin_sec_log10, CAI, geneLength_codon_log10) %>%
    `colnames<-`(c(\ORF\, \paraCombo\, \RMTE\, \log(mRNAabundance)\, \log(IniProb)\, \log(DecRate)\, \CAI\, \log(Length)\)) %>%
    pivot_longer(cols = -c(RMTE, paraCombo, ORF), names_to = \feature\, values_to = \featureVal\) %>%
    ggplot(aes(x=featureVal, y=RMTE, color=paraCombo)) +
    geom_point(size=0.5) +
    facet_wrap(~feature, scales = \free\, nrow=1, strip.position = \bottom\) +
    theme_bw() +
    scale_y_log10() +
    theme(text = element_text(size = 13),
              legend.position = \none\,
              strip.text = element_text(color=\black\, size=rel(1.2)),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank(),
              strip.background = element_blank(),  
              strip.placement = \outside\     #to put the strip labels below x-axis
      ) +
        labs(x = \\,
             y = \Relative mean translation efficiency\,
             title = \dc_0_r_1\) +
        geom_smooth(method=\lm\) +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_1\, mRNADecRateNeymotin_sec != 0, mRNAabundance>1, ORF!=\YER053C-A\, ORF!=\YLR106C\) %>%  #remove the gene with HL=450min, also an outlier gene \YLR106C\ with RMTE==0.6
    select(ORF, paraCombo, RMTE, mRNAabundance_log10, iniProb_log10, mRNADecRateNeymotin_sec_log10, CAI, geneLength_codon_log10) %>%
    `colnames<-`(c(\ORF\, \paraCombo\, \RMTE\, \log(mRNAabundance)\, \log(IniProb)\, \log(DecRate)\, \CAI\, \log(Length)\)) %>%
    pivot_longer(cols = -c(RMTE, paraCombo, ORF), names_to = \feature\, values_to = \featureVal\) %>%
    ggplot(aes(x=featureVal, y=RMTE, color=paraCombo)) +
    geom_point(size=0.5) +
    facet_wrap(~feature, scales = \free\, nrow=1, strip.position = \bottom\) +
    theme_bw() +
    scale_y_log10() +
    theme(text = element_text(size = 13),
              legend.position = \none\,
              strip.text = element_text(color=\black\, size=rel(1.2)),  # can't use element_blank() here because it gives error when p1+p2
              plot.margin = margin(r = 0, unit = \pt\),
              panel.grid = element_blank(),
              strip.background = element_blank(),  
              strip.placement = \outside\     #to put the strip labels below x-axis
      ) +
        labs(x = \\,
             y = \Relative mean translation efficiency\,
             title = \dc_1_r_1\) +
        geom_smooth(method=\lm\) +
        stat_cor(aes(label=paste(..r.label..,  sep = \~`

`geom_smooth()` using formula 'y ~ x'

```r
tmpdf <- data.frame(lifetime = log10(as.numeric(dfplot[1, 6:5005])))
which.max(density(tmpdf$lifetime)$y)
density(tmpdf$lifetime)$x[387]
ggplot(tmpdf, aes(lifetime)) + geom_density() + geom_vline(xintercept = density(tmpdf$lifetime)$x[387]) 

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



Fig. S

NOTES: 
Output decay rate = 1/mean lifetime <https://en.wikipedia.org/wiki/Particle_decay>

Total output decay rate = output decay rate * output mean mRNA count

Total input decay rate = input decay rate * mRNAabundance

Total synthesis rate = a scaled constant

(The above rates are all gene-specific)

Summary: changing total output decay rate is a result of changing co-translational decay and ribosome protection. In order to maintain the constant mRNA level, we have to scale the total synthesis rates to counter-effect the changing output decay rates.

When co-translational decay is higher than 0, there's always a surplus of mRNAs marked for decay, that are not accounted when calculating total decay probabilities (They're not accounted because they already have decay machinery bound to them, so cannot be accounted again into the mRNA pool that can still be degraded). Therefore the synthesis rates are scaled, in order to maintain a stable relative mRNA count (= mean output mRNA count/mRNAabundance) for it to be close to 1 (Supp Fig 1). The mean output mRNA count for each gene is a result of the balance between its scaled synthesis rate and total output decay rate. In other words, the total output decay rate directly respond to the scaled synthesis rate, rather than the input decay rate. 

The scaled total synthesis rates are the smallest when co-translational decay = 100%, so that the total real-time mRNA count can be close to mRNAabundance even with the highest amount of mRNAs marked for decay. In contrast, the scaled total synthesis rates are the largest and equal to the total input decay rates when co-trans decay = 0% (Supp Fig 2a), because there is 0 mRNA marked for decay. 

Hypothetically, the total output decay rates should equal to the total synthesis rates in order to maintain the real-time mRNA count to be largely the same at the beginning and the end of the simulation. If unequal, the real-time mRNA count should either go up or down as the simulation continues compared to the begining amount (i.e. mRNAabundance). However, our results show that the total output decay rates are slightly larger than the scaled synthesis rates across all the parameter combos (Supp Fig 2b. this is the part I don't understand, is it coz the total output decay rates include considering mRNAsMarkedForDecay, but the total scaled synthesis rates doesn't include mRNAsMarkedForDecay), and that's somehow contributing to the mean real-time mRNA count being equal to mRNAabundance as shown in Supp Fig 1. 

Because the output decay rates directly respond to total synthesis rates, they are influenced by the para combos in the same way that total synthesis rates are. That explains why Supp Fig 2c is similar to Supp Fig 2a in that the peaks skew to the right as co-translational decay ratio goes down. However in Supp Fig 2a, the ratio (scaled synthesis rates V.S. total input decay rates) skewed from less than 1 to 1; while in Supp Fig 2c, the ratio (total output decay rates V.S. total input decay rates) skewed from 1 to larger than 1. This would make sense if we can explain what's going on in Supp Fig 2b. 

Meanwhile, the output decay rates for short genes are much less affected by different co-translational decay proportions than long genes (Supp Fig 2d). This should be a reflection that the total synthesis rates for short genes are changing less with changing co-translational decay.

Supp Fig 2a: The scaled synthesis rates are the smallest when co-trans decay = 100%, while they\nare the largest and equal to the total input decay rates when co-trans decay = 0% 

Supp Fig 2b: Total output mRNA decay rates are slightly larger than scaled mRNA synthesis rates \n, but the ratio stays stable with changing parameters \n(I don't understand why the decay rates have to be larger)

Supp Fig 2c: Output decay rates respond to scaled synthesis rates, therefore its ratio against \ntotal input decay rates shows similar patter as scaled synthesis rates in Fig 2

Supp Fig 2d: Output decay rates for short genes are much less affected by different \nco-translational decay than long genes

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin {"data":"```r\n```r\nallSimInputOutput_trial <- read_feather(\\../../../exp0/externalData/exp11_outcomeAll.feather\\)\ntmp <- allSimInputOutput_trial %>% \n  filter(combo != \\mRNAconstant\\, w != 0.01, w != 0.05, w != 0.2) %>% \n  select(NMMC, combo, ORF, r, w) %>% # NMMC in trial = RMMC in exp0, \\normalized\\ VS \\relative\\\n  mutate(dc=1-as.numeric(r)) %>%\n  select(-r) %>% #delete the original column named \\r\\, in trial data the r means CMD levels, and w means ribosome protection index\n  dplyr::rename(\\r\\=\\w\\, \\paraCombo\\=\\combo\\, \\RMMCtrial\\=\\NMMC\\) %>% # change the original col named \\w\\ into \\r\\, b/c \\r\\ in exp0 means ribosome protection index\n  mutate(paraCombo=paste0(\\dc_\\, dc, \\_r_\\, r)) %>%\n  select(-dc, -r)\n  \n    \nsup1 <- (allSimInputOutput %>%\n    filter(paraCombo != \\mRNAconstant\\) %>%\n    left_join(., tmp, by = c(\\paraCombo\\=\\paraCombo\\, \\ORF\\=\\ORF\\))) %>%\n    select(\\paraCombo\\, \\RMMC\\, \\RMMCtrial\\, \\dc\\, \\r\\) %>%\n    dplyr::rename(\\AfterScaling\\=\\RMMC\\, \\BeforeScaling\\=\\RMMCtrial\\) %>%\n    pivot_longer(names_to = \\key\\, values_to = \\RMMCvalue\\, cols = 2:3) %>%\n    ggplot(aes(x=RMMCvalue, color=key)) +\n        geom_density() +\n        facet_grid(dc~r, labeller=labeller(dc=c(\\0\\=\\0%\\, \\0.2\\=\\20%\\, \\0.4\\=\\40%\\, \\0.6\\=\\60%\\, \\0.8\\=\\80%\\, \\1\\=\\CMD 100%\\), r=c(\\0\\=\\Ribo prot indx = 0\\, \\0.1\\=\\0.1\\, \\0.4\\=\\0.4\\, \\1\\=\\1\\), label_parsed)) + \n        labs(title = \\Sup Fig 1: mRNA counts when varying are laregely equal to when they are constant \\nafter scaling the mRNA synthesis rates\\, \n             x=\\Relative mean mRNA count\\) +\n        scale_y_continuous() +\n        geom_vline(xintercept =1, linetype=\\dashed\\, color=\\dark grey\\) +\n        theme_bw() +\n        theme(legend.title = element_blank(),\n              legend.position = \\bottom\\,\n              strip.text.x = element_text(size=10),\n              strip.text.y = element_text(size=10)) # make the gridline color darker\n\n\n\n############################################ plotting the (total syn rate after scaling)/(input decay rate*mRNAabundance) for all genes for all rw combos\noutdf <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839))\n\nfor (i in 1:(nrow(dc.r.df)-1)) \n{\n    inf <- read.table(paste0(\\../../simulations/simInput/allGenesDecrEqualsSynrWithScaling_\\, dc.r.df$paraCombo[i+1], \\_S.cer.mRNA.ini.abndc.syn.dec.tsv\\))\n    outdf[, i] <- inf[, 3]/(inf[, 4]*inf[, 2])\n}\n\ncolnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]\n\nsup2a <- outdf %>%\n  pivot_longer(names_to = \\paraCombo\\, values_to = \\value\\, cols = 1:(nrow(dc.r.df)-1)) %>%\n  left_join(., dc.r.df, by = \\paraCombo\\) %>%\n  filter(value != 0, paraCombo == \\dc_1_r_0\\|paraCombo == \\dc_0_r_0\\|paraCombo == \\dc_1_r_1\\|paraCombo == \\dc_0_r_1\\) %>%\n  ggplot(aes(x=value)) +\n    geom_density() +\n    facet_grid(dc~r, labeller=labeller(dc=c(\\0\\=\\0%\\, \\0.2\\=\\20%\\, \\0.4\\=\\40%\\, \\0.6\\=\\60%\\, \\0.8\\=\\80%\\, \\1\\=\\CMD 100%\\), r=c(\\0\\=\\Ribo prot indx = 0\\, \\0.1\\=\\0.1\\, \\0.4\\=\\0.4\\, \\1\\=\\1\\), label_parsed)) +\n    geom_vline(xintercept =1, linetype=\\dashed\\, color=\\dark grey\\) +\n    labs(title = \\\\,\n         x=\\Scaled synthesis rates/Total input decay rates\\) +\n    theme_bw() +\n    theme(strip.text.x = element_text(size=9),\n          strip.text.y = element_blank()) +\n    scale_x_continuous(breaks = c(0.9, 1, 1.1, 1.3, 1.5))\n\n############################################## plotting the (output decay rate)/(syn rate after scaling) for all genes for all dc/r paraCombos\nmmdrAllgeneAlldcr <- read_feather(\\../calculatedMetrics/mmdrAllgeneAlldcr.feather\\)[, 2:nrow(dc.r.df)]\nmmcAllgeneAlldcr<- read_feather(\\../calculatedMetrics/mmcAllgeneAlldcr.feather\\)[, 2:nrow(dc.r.df)]\ntotdr <- mmdrAllgeneAlldcr*mmcAllgeneAlldcr\n\ntotsynr <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839))\nfor (i in 1:(nrow(dc.r.df)-1))\n{\n    inf <- read.table(paste0(\\../../simulations/simInput/allGenesDecrEqualsSynrWithScaling_\\, dc.r.df$paraCombo[i+1], \\_S.cer.mRNA.ini.abndc.syn.dec.tsv\\))\n    totsynr[, i] <- inf[, 3]\n}\n\nratio.dr.synr <- totdr/totsynr\ncolnames(ratio.dr.synr) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]\nratio.dr.synr <- ratio.dr.synr[-1278, ]   # gene with the HL-min=490 min, the data point skews the scale\n\nsup2b <- ratio.dr.synr %>%\n  pivot_longer(names_to = \\paraCombo\\, values_to = \\value\\, cols = 1:(nrow(dc.r.df)-1)) %>%\n  left_join(., dc.r.df, by = \\paraCombo\\) %>%\n  filter(paraCombo == \\dc_1_r_0\\|paraCombo == \\dc_0_r_0\\|paraCombo == \\dc_1_r_1\\|paraCombo == \\dc_0_r_1\\) %>% \n  filter(!is.na(value)) %>%\n  ggplot(aes(x=value)) +\n    geom_density() +\n    facet_grid(dc~r, labeller=labeller(dc=c(\\0\\=\\0%\\, \\0.2\\=\\20%\\, \\0.4\\=\\40%\\, \\0.6\\=\\60%\\, \\0.8\\=\\80%\\, \\1\\=\\CMD 100%\\), r=c(\\0\\=\\Ribo prot indx = 0\\, \\0.1\\=\\0.1\\, \\0.4\\=\\0.4\\, \\1\\=\\1\\), label_parsed)) +\n    labs(title = \\\\, \n         x=\\Total output decay rates/Scaled synthesis rates\\) +\n    geom_vline(xintercept =1, linetype=\\dashed\\, color=\\dark grey\\) +\n    theme_bw() +\n    theme(strip.text.x = element_text(size=9),\n          strip.text.y = element_text(size=9),\n          axis.title.y = element_blank()) \n\n\n############################################ plotting the (output decay rate*real time mRNA)/(input decay rate*mRNAabundance)for all genes for all rw combos\noutdf <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839)) #contains all the total input decay rates\n\nfor (i in 1:(nrow(dc.r.df)-1))  # these 16 columns are all the same, all equal to simInputFeatures$mRNAabundance*simInputFeatures$mRNADecRateNeymotin_sec\n{\n    inf <- read.table(paste0(\\../../simulations/simInput/allGenesDecrEqualsSynrWithScaling_\\, dc.r.df$paraCombo[i+1], \\_S.cer.mRNA.ini.abndc.syn.dec.tsv\\))\n    outdf[, i] <- (inf[, 4]*inf[, 2])\n}\n\noutdf <- totdr/outdf\ncolnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]\n\nsup2c <- outdf[-1278, ] %>%       # gene with the HL-min=490 min, the data point skews the scales\n  pivot_longer(names_to = \\paraCombo\\, values_to = \\value\\, cols = 1:(nrow(dc.r.df)-1)) %>%\n  left_join(., dc.r.df, by = \\paraCombo\\) %>%\n  filter(value != 0, paraCombo == \\dc_1_r_0\\|paraCombo == \\dc_0_r_0\\|paraCombo == \\dc_1_r_1\\|paraCombo == \\dc_0_r_1\\) %>%\n  ggplot(aes(x=value)) +\n    geom_density() +\n    facet_grid(dc~r, labeller=labeller(dc=c(\\0\\=\\0%\\, \\0.2\\=\\20%\\, \\0.4\\=\\40%\\, \\0.6\\=\\60%\\, \\0.8\\=\\80%\\, \\1\\=\\CMD 100%\\), r=c(\\0\\=\\Ribo prot indx = 0\\, \\0.1\\=\\0.1\\, \\0.4\\=\\0.4\\, \\1\\=\\1\\), label_parsed)) +\n    geom_vline(xintercept =1, linetype=\\dashed\\, color=\\dark grey\\) +\n    labs(title = \\\\,\n         x=\\Total output decay rates/Total input decay rates\\) +\n    theme_bw() +\n    theme(#strip.text.x = element_text(size=8),\n          strip.text.y = element_blank(),\n          strip.text.x = element_blank())  # hide the grid title\n\n\n############################################\n# sl = short or long\noutdf$sl <- rep(NA,4839)   \noutdf$sl[which(simInputFeatures$geneLength_codon>512)] <- \\Long\\\noutdf$sl[which(simInputFeatures$geneLength_codon<=512)] <- \\Short\\\n\n\nsup2d <- outdf[-1278, ] %>%       # took the gene with the HL-min=490 min, the data point skews the scales\n  pivot_longer(names_to = \\paraCombo\\, values_to = \\value\\, cols = 1:(nrow(dc.r.df)-1)) %>%\n  left_join(., dc.r.df, by = \\paraCombo\\) %>%\n  filter(value != 0, paraCombo == \\dc_1_r_0\\|paraCombo == \\dc_0_r_0\\|paraCombo == \\dc_1_r_1\\|paraCombo == \\dc_0_r_1\\) %>%\n  ggplot(aes(x=value, color=sl, group=sl)) +\n    geom_density() +\n    facet_grid(dc~r, labeller=labeller(dc=c(\\0\\=\\0%\\, \\0.2\\=\\20%\\, \\0.4\\=\\40%\\, \\0.6\\=\\60%\\, \\0.8\\=\\80%\\, \\1\\=\\CMD 100%\\), r=c(\\0\\=\\Ribo prot indx = 0\\, \\0.1\\=\\0.1\\, \\0.4\\=\\0.4\\, \\1\\=\\1\\), label_parsed)) +\n    labs(title = \\\\,\n         x=\\Total output decay rates/Total input decay rates\\) +\n    geom_vline(xintercept =1, linetype=\\dashed\\, color=\\dark grey\\) +\n    theme_bw() +\n    theme(\n          strip.text.y = element_text(size=9),\n          strip.text.x = element_blank(),\n          legend.position = c(0.9, 0.95),\n          axis.title.y = element_blank(),\n          legend.background=element_blank(), # change the legend background to transparent\n          legend.key.size = unit(0.5, \\line\\)) +\n    scale_color_discrete(name = \\\\, labels = )\n############################################\nsup1\n\nsup2 <- (sup2a|sup2b)/(sup2c|sup2d)\nsup2 + plot_annotation(\n   title = 'Supp Fig 2.',\n  # subtitle = 'Supp Fig 2.',\n  # caption = 'Hello',   # this one appears at the bottom\n  tag_levels = 'a'\n)\n\n\n\n\n####################################\ntot.in.dr <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839)) #contains all the total input decay rates\n\nfor (i in 1:(nrow(dc.r.df)-1))  # these 16 columns are all the same, all equal to simInputFeatures$mRNAabundance*simInputFeatures$mRNADecRateNeymotin_sec\n{\n    inf <- read.table(paste0(basefolder, expID, \\/input/allGenesDecrEqualsSynrWithScaling_\\, dc.r.df$paraCombo[i+1], \\_S.cer.mRNA.ini.abndc.syn.dec\\))\n    tot.in.dr[, i] <- (inf[, 4]*inf[, 2])\n}\n\nmmdrAllgeneAlldcr <- read_feather(paste0(basefolder, expID, \\/analyses/featherFiles/mmdrAllgeneAlldcr.feather\\))[, 2:nrow(dc.r.df)]\nmmcAllgeneAlldcr<- read_feather(paste0(basefolder, expID, \\/analyses/featherFiles/mmcAllgeneAlldcr.feather\\))[, 2:nrow(dc.r.df)]\ntot.out.dr <- mmdrAllgeneAlldcr*mmcAllgeneAlldcr\n\noutdf <- tot.out.dr/tot.in.dr\ncolnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]\n\noutdf[-1278, ] %>%       # gene with the HL-min=490 min, the data point skews the scales\n  pivot_longer(names_to = \\paraCombo\\, values_to = \\outInDrRatio\\, cols = 1:(nrow(dc.r.df)-1)) %>%\n  left_join(., dc.r.df, by = \\paraCombo\\) %>%\n  filter(outInDrRatio != 0) %>%\n  mutate(dc_fct=factor(dc, levels=c(\\1\\, \\0.8\\, \\0.6\\, \\0.4\\, \\0.2\\, \\0\\))) %>%\n  ggplot(aes(x=dc_fct, y=outInDrRatio, fill=r)) +\n  geom_boxplot(outlier.size = 0.1) +\n  geom_hline(aes(yintercept=median(as.numeric(unlist(outdf %>%select(dc_1_r_0)))[-1278], na.rm = TRUE)), \n             color=\\red\\, linetype=\\dashed\\) +\n  theme_bw() +\n  labs(x=\\CMD level\\,\n       y=\\Ratio between total output/input decay rates\\) +\n  scale_x_discrete(labels=c(\\1\\=\\100%\\, \\0.8\\=\\80%\\, \\0.6\\=\\60%\\, \\0.4\\=\\40%\\, \\0.2\\=\\20%\\, \\0\\=\\0\\)) +\n  scale_fill_discrete(name = \\Ribosome\\nprot index\\) +\n  theme(\n    text = element_text(size = 13),\n    legend.position = \\none\\\n  )\n\n\n##############\noutdf <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839)) #contains all the total input decay rates\n\nfor (i in 1:(nrow(dc.r.df)-1))  # these 16 columns are all the same, all equal to simInputFeatures$mRNAabundance*simInputFeatures$mRNADecRateNeymotin_sec\n{\n    inf <- read.table(paste0(basefolder, expID, \\/input/allGenesDecrEqualsSynrWithScaling_\\, dc.r.df$paraCombo[i+1], \\_S.cer.mRNA.ini.abndc.syn.dec\\))\n    outdf[, i] <- (inf[, 4]*inf[, 2])\n}\n\n\noutdf <- totdr/outdf\ncolnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]\n\n\noutdf %>%\n  mutate(mRNAabundance=simInputFeatures$mRNAabundance, mRNADecRateNeymotin_sec=simInputFeatures$mRNADecRateNeymotin_sec, ORF=simInputFeatures$ORF) %>%\n  pivot_longer(names_to = \\paraCombo\\, values_to = \\ratioVal\\, cols = !c(mRNAabundance, mRNADecRateNeymotin_sec, ORF)) %>%\n  filter(paraCombo == \\dc_0_r_1\\, mRNADecRateNeymotin_sec != 0, ORF != \\YER053C-A\\) %>%\n  ggplot(aes(x=mRNAabundance, y=ratioVal)) +\n  geom_point() +\n  theme_bw() +\n  scale_x_log10()\n  \n```\n```"} -->

```r
```r
allSimInputOutput_trial <- read_feather(\../../../exp0/externalData/exp11_outcomeAll.feather\)
tmp <- allSimInputOutput_trial %>% 
  filter(combo != \mRNAconstant\, w != 0.01, w != 0.05, w != 0.2) %>% 
  select(NMMC, combo, ORF, r, w) %>% # NMMC in trial = RMMC in exp0, \normalized\ VS \relative\
  mutate(dc=1-as.numeric(r)) %>%
  select(-r) %>% #delete the original column named \r\, in trial data the r means CMD levels, and w means ribosome protection index
  dplyr::rename(\r\=\w\, \paraCombo\=\combo\, \RMMCtrial\=\NMMC\) %>% # change the original col named \w\ into \r\, b/c \r\ in exp0 means ribosome protection index
  mutate(paraCombo=paste0(\dc_\, dc, \_r_\, r)) %>%
  select(-dc, -r)
  
    
sup1 <- (allSimInputOutput %>%
    filter(paraCombo != \mRNAconstant\) %>%
    left_join(., tmp, by = c(\paraCombo\=\paraCombo\, \ORF\=\ORF\))) %>%
    select(\paraCombo\, \RMMC\, \RMMCtrial\, \dc\, \r\) %>%
    dplyr::rename(\AfterScaling\=\RMMC\, \BeforeScaling\=\RMMCtrial\) %>%
    pivot_longer(names_to = \key\, values_to = \RMMCvalue\, cols = 2:3) %>%
    ggplot(aes(x=RMMCvalue, color=key)) +
        geom_density() +
        facet_grid(dc~r, labeller=labeller(dc=c(\0\=\0%\, \0.2\=\20%\, \0.4\=\40%\, \0.6\=\60%\, \0.8\=\80%\, \1\=\CMD 100%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) + 
        labs(title = \Sup Fig 1: mRNA counts when varying are laregely equal to when they are constant \nafter scaling the mRNA synthesis rates\, 
             x=\Relative mean mRNA count\) +
        scale_y_continuous() +
        geom_vline(xintercept =1, linetype=\dashed\, color=\dark grey\) +
        theme_bw() +
        theme(legend.title = element_blank(),
              legend.position = \bottom\,
              strip.text.x = element_text(size=10),
              strip.text.y = element_text(size=10)) # make the gridline color darker



############################################ plotting the (total syn rate after scaling)/(input decay rate*mRNAabundance) for all genes for all rw combos
outdf <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839))

for (i in 1:(nrow(dc.r.df)-1)) 
{
    inf <- read.table(paste0(\../../simulations/simInput/allGenesDecrEqualsSynrWithScaling_\, dc.r.df$paraCombo[i+1], \_S.cer.mRNA.ini.abndc.syn.dec.tsv\))
    outdf[, i] <- inf[, 3]/(inf[, 4]*inf[, 2])
}

colnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]

sup2a <- outdf %>%
  pivot_longer(names_to = \paraCombo\, values_to = \value\, cols = 1:(nrow(dc.r.df)-1)) %>%
  left_join(., dc.r.df, by = \paraCombo\) %>%
  filter(value != 0, paraCombo == \dc_1_r_0\|paraCombo == \dc_0_r_0\|paraCombo == \dc_1_r_1\|paraCombo == \dc_0_r_1\) %>%
  ggplot(aes(x=value)) +
    geom_density() +
    facet_grid(dc~r, labeller=labeller(dc=c(\0\=\0%\, \0.2\=\20%\, \0.4\=\40%\, \0.6\=\60%\, \0.8\=\80%\, \1\=\CMD 100%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) +
    geom_vline(xintercept =1, linetype=\dashed\, color=\dark grey\) +
    labs(title = \\,
         x=\Scaled synthesis rates/Total input decay rates\) +
    theme_bw() +
    theme(strip.text.x = element_text(size=9),
          strip.text.y = element_blank()) +
    scale_x_continuous(breaks = c(0.9, 1, 1.1, 1.3, 1.5))

############################################## plotting the (output decay rate)/(syn rate after scaling) for all genes for all dc/r paraCombos
mmdrAllgeneAlldcr <- read_feather(\../calculatedMetrics/mmdrAllgeneAlldcr.feather\)[, 2:nrow(dc.r.df)]
mmcAllgeneAlldcr<- read_feather(\../calculatedMetrics/mmcAllgeneAlldcr.feather\)[, 2:nrow(dc.r.df)]
totdr <- mmdrAllgeneAlldcr*mmcAllgeneAlldcr

totsynr <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839))
for (i in 1:(nrow(dc.r.df)-1))
{
    inf <- read.table(paste0(\../../simulations/simInput/allGenesDecrEqualsSynrWithScaling_\, dc.r.df$paraCombo[i+1], \_S.cer.mRNA.ini.abndc.syn.dec.tsv\))
    totsynr[, i] <- inf[, 3]
}

ratio.dr.synr <- totdr/totsynr
colnames(ratio.dr.synr) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]
ratio.dr.synr <- ratio.dr.synr[-1278, ]   # gene with the HL-min=490 min, the data point skews the scale

sup2b <- ratio.dr.synr %>%
  pivot_longer(names_to = \paraCombo\, values_to = \value\, cols = 1:(nrow(dc.r.df)-1)) %>%
  left_join(., dc.r.df, by = \paraCombo\) %>%
  filter(paraCombo == \dc_1_r_0\|paraCombo == \dc_0_r_0\|paraCombo == \dc_1_r_1\|paraCombo == \dc_0_r_1\) %>% 
  filter(!is.na(value)) %>%
  ggplot(aes(x=value)) +
    geom_density() +
    facet_grid(dc~r, labeller=labeller(dc=c(\0\=\0%\, \0.2\=\20%\, \0.4\=\40%\, \0.6\=\60%\, \0.8\=\80%\, \1\=\CMD 100%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) +
    labs(title = \\, 
         x=\Total output decay rates/Scaled synthesis rates\) +
    geom_vline(xintercept =1, linetype=\dashed\, color=\dark grey\) +
    theme_bw() +
    theme(strip.text.x = element_text(size=9),
          strip.text.y = element_text(size=9),
          axis.title.y = element_blank()) 


############################################ plotting the (output decay rate*real time mRNA)/(input decay rate*mRNAabundance)for all genes for all rw combos
outdf <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839)) #contains all the total input decay rates

for (i in 1:(nrow(dc.r.df)-1))  # these 16 columns are all the same, all equal to simInputFeatures$mRNAabundance*simInputFeatures$mRNADecRateNeymotin_sec
{
    inf <- read.table(paste0(\../../simulations/simInput/allGenesDecrEqualsSynrWithScaling_\, dc.r.df$paraCombo[i+1], \_S.cer.mRNA.ini.abndc.syn.dec.tsv\))
    outdf[, i] <- (inf[, 4]*inf[, 2])
}

outdf <- totdr/outdf
colnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]

sup2c <- outdf[-1278, ] %>%       # gene with the HL-min=490 min, the data point skews the scales
  pivot_longer(names_to = \paraCombo\, values_to = \value\, cols = 1:(nrow(dc.r.df)-1)) %>%
  left_join(., dc.r.df, by = \paraCombo\) %>%
  filter(value != 0, paraCombo == \dc_1_r_0\|paraCombo == \dc_0_r_0\|paraCombo == \dc_1_r_1\|paraCombo == \dc_0_r_1\) %>%
  ggplot(aes(x=value)) +
    geom_density() +
    facet_grid(dc~r, labeller=labeller(dc=c(\0\=\0%\, \0.2\=\20%\, \0.4\=\40%\, \0.6\=\60%\, \0.8\=\80%\, \1\=\CMD 100%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) +
    geom_vline(xintercept =1, linetype=\dashed\, color=\dark grey\) +
    labs(title = \\,
         x=\Total output decay rates/Total input decay rates\) +
    theme_bw() +
    theme(#strip.text.x = element_text(size=8),
          strip.text.y = element_blank(),
          strip.text.x = element_blank())  # hide the grid title


############################################
# sl = short or long
outdf$sl <- rep(NA,4839)   
outdf$sl[which(simInputFeatures$geneLength_codon>512)] <- \Long\
outdf$sl[which(simInputFeatures$geneLength_codon<=512)] <- \Short\


sup2d <- outdf[-1278, ] %>%       # took the gene with the HL-min=490 min, the data point skews the scales
  pivot_longer(names_to = \paraCombo\, values_to = \value\, cols = 1:(nrow(dc.r.df)-1)) %>%
  left_join(., dc.r.df, by = \paraCombo\) %>%
  filter(value != 0, paraCombo == \dc_1_r_0\|paraCombo == \dc_0_r_0\|paraCombo == \dc_1_r_1\|paraCombo == \dc_0_r_1\) %>%
  ggplot(aes(x=value, color=sl, group=sl)) +
    geom_density() +
    facet_grid(dc~r, labeller=labeller(dc=c(\0\=\0%\, \0.2\=\20%\, \0.4\=\40%\, \0.6\=\60%\, \0.8\=\80%\, \1\=\CMD 100%\), r=c(\0\=\Ribo prot indx = 0\, \0.1\=\0.1\, \0.4\=\0.4\, \1\=\1\), label_parsed)) +
    labs(title = \\,
         x=\Total output decay rates/Total input decay rates\) +
    geom_vline(xintercept =1, linetype=\dashed\, color=\dark grey\) +
    theme_bw() +
    theme(
          strip.text.y = element_text(size=9),
          strip.text.x = element_blank(),
          legend.position = c(0.9, 0.95),
          axis.title.y = element_blank(),
          legend.background=element_blank(), # change the legend background to transparent
          legend.key.size = unit(0.5, \line\)) +
    scale_color_discrete(name = \\, labels = )
############################################
sup1

sup2 <- (sup2a|sup2b)/(sup2c|sup2d)
sup2 + plot_annotation(
   title = 'Supp Fig 2.',
  # subtitle = 'Supp Fig 2.',
  # caption = 'Hello',   # this one appears at the bottom
  tag_levels = 'a'
)




####################################
tot.in.dr <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839)) #contains all the total input decay rates

for (i in 1:(nrow(dc.r.df)-1))  # these 16 columns are all the same, all equal to simInputFeatures$mRNAabundance*simInputFeatures$mRNADecRateNeymotin_sec
{
    inf <- read.table(paste0(basefolder, expID, \/input/allGenesDecrEqualsSynrWithScaling_\, dc.r.df$paraCombo[i+1], \_S.cer.mRNA.ini.abndc.syn.dec\))
    tot.in.dr[, i] <- (inf[, 4]*inf[, 2])
}

mmdrAllgeneAlldcr <- read_feather(paste0(basefolder, expID, \/analyses/featherFiles/mmdrAllgeneAlldcr.feather\))[, 2:nrow(dc.r.df)]
mmcAllgeneAlldcr<- read_feather(paste0(basefolder, expID, \/analyses/featherFiles/mmcAllgeneAlldcr.feather\))[, 2:nrow(dc.r.df)]
tot.out.dr <- mmdrAllgeneAlldcr*mmcAllgeneAlldcr

outdf <- tot.out.dr/tot.in.dr
colnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]

outdf[-1278, ] %>%       # gene with the HL-min=490 min, the data point skews the scales
  pivot_longer(names_to = \paraCombo\, values_to = \outInDrRatio\, cols = 1:(nrow(dc.r.df)-1)) %>%
  left_join(., dc.r.df, by = \paraCombo\) %>%
  filter(outInDrRatio != 0) %>%
  mutate(dc_fct=factor(dc, levels=c(\1\, \0.8\, \0.6\, \0.4\, \0.2\, \0\))) %>%
  ggplot(aes(x=dc_fct, y=outInDrRatio, fill=r)) +
  geom_boxplot(outlier.size = 0.1) +
  geom_hline(aes(yintercept=median(as.numeric(unlist(outdf %>%select(dc_1_r_0)))[-1278], na.rm = TRUE)), 
             color=\red\, linetype=\dashed\) +
  theme_bw() +
  labs(x=\CMD level\,
       y=\Ratio between total output/input decay rates\) +
  scale_x_discrete(labels=c(\1\=\100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\0\)) +
  scale_fill_discrete(name = \Ribosome\nprot index\) +
  theme(
    text = element_text(size = 13),
    legend.position = \none\
  )


##############
outdf <- tibble(matrix(NA, ncol=nrow(dc.r.df)-1, nrow=4839)) #contains all the total input decay rates

for (i in 1:(nrow(dc.r.df)-1))  # these 16 columns are all the same, all equal to simInputFeatures$mRNAabundance*simInputFeatures$mRNADecRateNeymotin_sec
{
    inf <- read.table(paste0(basefolder, expID, \/input/allGenesDecrEqualsSynrWithScaling_\, dc.r.df$paraCombo[i+1], \_S.cer.mRNA.ini.abndc.syn.dec\))
    outdf[, i] <- (inf[, 4]*inf[, 2])
}


outdf <- totdr/outdf
colnames(outdf) <- dc.r.df$paraCombo[2:nrow(dc.r.df)]


outdf %>%
  mutate(mRNAabundance=simInputFeatures$mRNAabundance, mRNADecRateNeymotin_sec=simInputFeatures$mRNADecRateNeymotin_sec, ORF=simInputFeatures$ORF) %>%
  pivot_longer(names_to = \paraCombo\, values_to = \ratioVal\, cols = !c(mRNAabundance, mRNADecRateNeymotin_sec, ORF)) %>%
  filter(paraCombo == \dc_0_r_1\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\) %>%
  ggplot(aes(x=mRNAabundance, y=ratioVal)) +
  geom_point() +
  theme_bw() +
  scale_x_log10()
  

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Fig. S

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```r
```r
l %>%
  filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YCR024C-B\, mRNAabundance != 1) %>%
  mutate(relativeFano = RMVPS/RMPSR) %>%
  ggplot(aes(x=RMPSR, y=relativeFano)) +
  geom_point(size=0.1) +
  scale_x_log10() +
  scale_y_log10() +
      labs(x= \Realtive mean protein synthesis rate\,
        y = \Relative fano factor in protein synthesis rate\,
        title = \\) +
    geom_smooth(method=\lm\) + 
    stat_cor(aes(label=paste(..r.label.., sep = \~`

```r
allSimInputOutput %>%
    filter(paraCombo == \dc_1_r_0\, mRNADecRateNeymotin_sec != 0, ORF != \YCR024C-B\, mRNAabundance != 1) %>%
    ggplot(aes(x=MRD, y=RMVTE)) +
    geom_point(size=0.1) +
    scale_x_log10() +
    scale_y_log10() +
    labs(x= \Mean ribosome density\,
        y = \Relative mean of variance in translation efficiency\,
        title = \\) +
    geom_smooth(method=\lm\) + 
    stat_cor(aes(label=paste(..r.label.., sep = \~`

Figure S.

```r

# calculating the mean number of free ribos (averaged over simuTime and techReps) for all dc.r.paraCombos
# RMFR = relative mean free ribo
mfr <- c() 
for(i in 1:nrow(dc.r.df)) # loop through the dc/r parameter space to calculate the mean free ribo
{
    # free_ribo averaged by simuTime and techReps
    mfr <- c(mfr, mean(h5read(simOutputH5, paste(\free_ribo_tRNA_perMin\, dc.r.df$paraCombo[i], \ribo\, sep=\/\)), na.rm = TRUE))
    # set na.rm=T because in rep 66 again there's an NA at the last minute, which won't affect the overall mean
}

rmfr <- mfr/mfr[1]
rmfr_df <- data.frame(RMFR = rmfr,
                      MFR = mfr,
                      para = dc.r.df$para)

# rmfr_df can be found in exp0_prepPlots.Rmd
rmfr_df %>%
  left_join(., dc.r.df, by =\para\) %>%
  filter(paraCombo != \mRNAconstant\) %>%
  select(RMFR, dc, r) %>%
  spread(dc, RMFR) %>%
  select(-r) %>%
  colMeans() %>%
  tibble() %>% 
  mutate(dc=as.character(seq(0, 1, by=0.2))) %>%
  `colnames<-`(c(\RMFRmeanByCoTrans\, \dc\)) %>%
    mutate(cotrans = paste0(as.numeric(dc)*100, \%\)) %>%
    ggplot(aes(x=cotrans, y=RMFRmeanByCoTrans)) +
    geom_point() +
    theme_bw() +
    labs ( title = \\,
                x =\Co-translational decay proportion\,
                y = \Mean free ribosomes (relative to mRNA constant)\) +
    theme(text = element_text(size = 13),
          panel.grid = element_blank()) +
    scale_x_discrete(limits = c(\100%\, \80%\, \60%\, \40%\, \20%\, \0%\))
  

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```r
```r

make_heatmap <- function(orderVar, outputVar, legendName, titleY)
{
    colorvec <- quantile(allSimInputOutput[[outputVar]])
  
    tmp <- (allSimInputOutput%>%filter(paraCombo == \dc_1_r_0\))[, c(\ORF\, orderVar)]
    tmp <- tmp[order(tmp[, 2], decreasing = T), ]
    tmp$newid_dc1r0 <- 1:4839
    
    allSimInputOutput %>% 
        filter(paraCombo != \mRNAconstant\) %>% 
        left_join(., tmp[, c(\ORF\, \newid_dc1r0\)], by = \ORF\) %>%
        mutate(dc_fct=factor(dc, levels=c(\1\, \0.8\, \0.6\, \0.4\, \0.2\, \0\))) %>%
        ggplot(aes_string(x = \r\, 
                   y = \factor(newid_dc1r0)\,
                   fill = outputVar)) +
        geom_raster() +
        theme_bw() +
        scale_fill_gradientn(colours = c(\#377eb8\, \white\, \#e41a1c\),   # blue white red
                        values = scales::rescale(colorvec), name=legendName) +
        facet_wrap(~dc_fct, nrow=1, labeller=labeller(dc_fct=c(\1\=\Co-trans 100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\0\))) + 
        labs ( x = \Ribosome protection index r\,
               y = titleY,
               title = \\) +
        theme(text = element_text(size = 18),
              axis.text.y = element_blank(),
              axis.ticks.y = element_blank(),
              panel.border = element_blank(),  # remove the grid lines in facet_grid
              panel.spacing=unit(0, \lines\)) +   # reset the spacing between facets
         guides(fill = guide_colourbar(barwidth = 1, barheight = 10)) +
         scale_x_discrete(expand = c(0, 0)) # remove the extra space beyond xlim

}

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```r
```r
# 1st var= the variable column the sorting is based on
# 2nd var= the variable column presented in the heatmap
# 3rd var= the legend text
# 4th var= y-axis title

make_heatmap(\RMTE\, \RMTE\, \Relative\nmean translation\nefficiency\, \Genes ranked by the first column\)
make_heatmap(\RMVTE\, \RMVTE\, \Relative mean\nof the variance\nin translation\nefficiency\, \Genes ranked by the first column\)

########################################################## add a plot for mRNAs marked for decay
colorvec <- quantile((allSimInputOutput%>% filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\))$MMDP)

tmp <- (allSimInputOutput%>%filter(paraCombo == \dc_1_r_0\))[, c(\ORF\, \RMTE\)]
tmp <- tmp[order(tmp[, 2], decreasing = T), ]
tmp$newid_dc1r0 <- 1:4839

allSimInputOutput %>% 
    mutate(dc_fct=factor(dc, levels=c(\1\, \0.8\, \0.6\, \0.4\, \0.2\, \0\))) %>%
    filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, dc != 0) %>% 
    left_join(., tmp[, c(\ORF\, \newid_dc1r0\)], by = \ORF\) %>%
    ggplot(aes(x = r, 
               y = factor(newid_dc1r0),
               #y = as.character(newid_geneLength_codon),   # genes are sorted by geneLength_codon within each group
               fill = MMDP)) +
    geom_raster() +
    theme_bw() +
    scale_fill_gradientn(colours = c(\#377eb8\, \white\, \#e41a1c\),   # blue white red
                    values = scales::rescale(colorvec), name=\mRNAs marked\nfor decay ratio\) +
    facet_wrap(~dc_fct, nrow=1, labeller=labeller(dc_fct=c(\1\=\Co-trans 100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\0%\))) +
    labs ( x = \Ribosome protection index w\,
           y = \Genes ranked by the first column in translation efficiency plot\,
           title = \\) +
    theme(text = element_text(size = 18),
          plot.title = element_text(size=rel(1.5)),
          axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          panel.border = element_blank(),  # remove the grid lines in facet_grid
          panel.spacing=unit(0, \lines\)) +   # reset the spacing between facets
     guides(fill = guide_colourbar(barwidth = 1, barheight = 10)) +
     scale_x_discrete(expand = c(0, 0)) # remove the extra space beyond xlim


########################################################## add another plot for mRNAs marked for decay
colorvec <- quantile((allSimInputOutput%>% filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\))$MMDP)

tmp <- (allSimInputOutput%>%filter(paraCombo == \dc_1_r_0\))[, c(\ORF\, \RMVTE\)]
tmp <- tmp[order(tmp[, 2], decreasing = T), ]
tmp$newid_dc1r0 <- 1:4839

allSimInputOutput %>% 
    mutate(dc_fct=factor(dc, levels=c(\1\, \0.8\, \0.6\, \0.4\, \0.2\, \0\))) %>%
    filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\, dc != 0) %>% 
    left_join(., tmp[, c(\ORF\, \newid_dc1r0\)], by = \ORF\) %>%
    ggplot(aes(x = r, 
               y = factor(newid_dc1r0),
               fill = MMDP)) +
    geom_raster() +
    theme_bw() +
    scale_fill_gradientn(colours = c(\#377eb8\, \white\, \#e41a1c\),   # blue white red
                    values = scales::rescale(colorvec), name=\mRNAs marked\nfor decay ratio\) +
    facet_wrap(~dc_fct, nrow=1, labeller=labeller(dc_fct=c(\1\=\Co-trans 100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\0%\))) +
    labs ( x = \Ribosome protection index w\,
           y = \Genes ranked by the first column in translation efficiency plot\,
           title = \\) +
    theme(text = element_text(size = 18),
          plot.title = element_text(size=rel(1.5)),
          axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          panel.border = element_blank(),  # remove the grid lines in facet_grid
          panel.spacing=unit(0, \lines\)) +   # reset the spacing between facets
     guides(fill = guide_colourbar(barwidth = 1, barheight = 10)) +
     scale_x_discrete(expand = c(0, 0)) # remove the extra space beyond xlim

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-->

```r
```r
# 1st var= the variable column the sorting is based on
# 2nd var= the variable column presented in the heatmap
# 3rd var= the legend text
# 4th var= y-axis title

make_heatmap(\RMPSR\, \RMPSR\, \Relative\nmean protein\nsyn rate\, \Genes ranked by the first column\)
make_heatmap(\RMVPS\, \RMVPS\, \Relative\nmean of variance in\nprotein syn rate\, \Genes ranked by the first column\)

make_heatmap_tmp <- function(orderVar, outputVar, legendName, titleY)
{
    colorvec <- quantile(allSimInputOutput[[outputVar]])
  
    tmp <- (allSimInputOutput%>%filter(paraCombo == \dc_1_r_0\))[, c(\ORF\, orderVar)]
    tmp <- tmp[order(tmp[, 2], decreasing = T), ]
    tmp$newid_dc1r0 <- 1:4839
    
    allSimInputOutput %>% 
        filter(paraCombo != \mRNAconstant\, RMVTBR != Inf) %>% 
        left_join(.,tmp[, c(\ORF\, \newid_dc1r0\)], by = \ORF\) %>%
        mutate(dc_fct=factor(dc, levels=c(\1\, \0.8\, \0.6\, \0.4\, \0.2\, \0\))) %>%
        ggplot(aes_string(x = \r\, 
                   y = \factor(newid_dc1r0)\,
                   fill = outputVar)) +
        geom_raster() +
        theme_bw() +
        scale_fill_gradientn(colours = c(\#377eb8\, \white\, \#e41a1c\),   # blue white red
                        values = scales::rescale(colorvec), name=legendName) +
        facet_wrap(~dc_fct, nrow=1, labeller=labeller(dc_fct=c(\1\=\Co-trans 100%\, \0.8\=\80%\, \0.6\=\60%\, \0.4\=\40%\, \0.2\=\20%\, \0\=\0\))) + 
        labs ( x = \Ribosome protection index r\,
               y = titleY,
               title = \\) +
        theme(text = element_text(size = 18),
              axis.text.y = element_blank(),
              axis.ticks.y = element_blank(),
              panel.border = element_blank(),  # remove the grid lines in facet_grid
              panel.spacing=unit(0, \lines\)) +   # reset the spacing between facets
         guides(fill = guide_colourbar(barwidth = 1, barheight = 10)) +
         scale_x_discrete(expand = c(0, 0)) # remove the extra space beyond xlim

}
make_heatmap_tmp(\RMTE\, \RMVTBR\, \Relative\nvariance in total\nbound ribosomes\, \Genes ranked by the first column in relative TE\)


make_heatmap(\MML\, \MML\, \Mean of mRNA\nlife-times\, \Genes ranked by the first column\)

###############################################################
colorvec <- quantile((allSimInputOutput%>% filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\))$RMRD)

tmp <- (allSimInputOutput%>%filter(paraCombo == \dc_1_r_0\))[, c(\ORF\, \RMTE\)]
tmp <- tmp[order(tmp[, 2], decreasing = T), ]
tmp$newid_dc1r0 <- 1:4839


allSimInputOutput %>% 
    filter(paraCombo != \mRNAconstant\, mRNADecRateNeymotin_sec != 0, ORF != \YER053C-A\) %>% 
    left_join(., tmp[, c(\ORF\, \newid_dc1r0\)], by = \ORF\) %>%
    ggplot(aes(x = r, 
               y = factor(newid_dc1r0),
               fill = RMRD)) +
    geom_raster() +
    theme_bw() +
    scale_fill_gradientn(colours = c(\#377eb8\, \white\, \#e41a1c\),   # blue white red
                    values = scales::rescale(colorvec), name=\Relative mean\nribo density\) +
    facet_wrap(~dc, nrow=1, labeller=labeller(dc=c(\1\=\Co-trans 100%\, \0.8\=\0.8\, \0.6\=\0.6\, \0.4\=\0.4\, \0.2\=\0.2\, \0\=\0\))) +
    labs ( x = \Ribosome protection index\,
           y = \Genes ranked by the first column for translation efficiency\,
           title = \\) +
    theme(text = element_text(size = 18),
          plot.title = element_text(size=rel(1.5)),
          axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          panel.border = element_blank(),  # remove the grid lines in facet_grid
          panel.spacing=unit(0, \lines\)) +   # reset the spacing between facets
     guides(fill = guide_colourbar(barwidth = 1, barheight = 10)) +
     scale_x_discrete(expand = c(0, 0)) # remove the extra space beyond xlim

```